Road surface conditions, such as a road surface consisting of lose gravel or smooth cobblestones, affect motor vehicles differently compared to road surfaces consisting of smooth asphalt or concrete.
According to DE 42 13 222 A1, the rolling noise of at least one wheel of a vehicle driven over the road surface is detected using a vehicle-mounted sensor. The sensor output signal is band-pass filtered to isolate a frequency range characteristic of road roughness. The effective value of the filtered signal is formed and low-pass filtered. The effective value is associated with a roughness value whilst compensating for the influence of other effects than roughness on the effective value. The roughness value is output to the driver and to control devices in the vehicle.
EP 1 978 490 A1 discloses a system for the recognition of the operating state of a vehicle engine based on the detection of an acoustic noise signal, for example a vibration signal, propagated in a compartment of the vehicle. The system provides for a comparison of a descriptor vector of the detected signals, having at least data indicative of the frequency and energy components of the signals, with a plurality of reference descriptor vectors indicative of known operating conditions acquired in a learning phase. The system furthermore provides for discriminating and recognizing a first class of signals indicative of an operating condition and a second class of signals indicative of an inoperative condition of the vehicle engine.
The object of the invention is to provide a robust and effective system for determining, with adequate processing resources, vibration characteristics of a motor vehicle.
The invention solves this object with the features described herein. The use of at least one support vector machine (SVM) is a robust and effective means for classifying the current vibration characteristic into at least one particular pre-set type of vibration characteristic. Support vector machines do not require excessive processing resources and can therefore be incorporated into vehicle-mounted electronic signal processing units with usual hardware and processing capabilities.
A support vector machine (SVM) is a mathematical model which has been obtained by training using a plurality of training data sets, each marked as belonging to a particular category or not. The SVM is then able to predict for an input new data set whether this new data set falls into this particular category or not. More specifically, the SVM is able to output a probability that the new data set falls into the particular category.
In a preferred application, the algorithm is adapted to determine wheel-induced vibration characteristics of a moving motor vehicle generated due to the rolling of a vehicle wheel on the ground. The invention may advantageously be used for classifying the current road surface as belonging to at least one particular pre-set type of road surface, for example, smooth road, cobbled pavement, concrete slabs, grass, and so on. However, the invention is not limited to this application. In addition or alternatively, vibrations originating from a vehicle part may be determined for diagnosis of the vehicle part. For example, wheel-induced vibration characteristics may be determined in order to detect unusual wheel conditions which may require special attention by the driver, like low tire pressure, unbalanced masses or tire types; or mounted snow chains. The invention is also applicable to determine vibrations characteristics induced by other vehicle parts such as the motor, gearbox or bearings. This may be useful for example in motor or gear-box diagnosis, or for detecting defect vehicle parts.
Preferably the classifying algorithm comprises a plurality of support vector machines, each of which is adapted to output a probability that the current vibration characteristic belongs to a particular one of different pre-set types of vibration characteristics. In this case, a plurality of different pre-set types of vibration characteristics can be identified. The support vector machines are preferably connected in parallel to each other. A decision means may advantageously be provided for determining the maximum probability among said probabilities output by said support vector machines.
Preferably, the sensing arrangement includes at least one vibration sensor adapted to detect structure-borne vibrations transmitted by the vehicle chassis. In a preferred embodiment, a vehicle impact sensor, in particular a frontal impact safing sensor, alternatively a side impact safing sensor, may be used as the vibration sensor. In this case, an additional vibration sensor is not required. Preferably the vibration sensor is positioned in a central region of the vehicle as seen from above. In this case, the vibration sensor is sensitive to vibrations from many different vehicle parts, for example from all four wheels.
Alternatively or in addition to the at least one vibration sensor, preferably at least one sound sensor adapted to detect air-borne acoustic rolling noise is provided. In this case, the sound sensor may preferably be used as a safing sensor to a vibration sensor of said sensing arrangement.
Information from the above described algorithm may preferably be used to control a vehicle safety means, as electronic stability control (ESC) system, anti-slipping system, active damping system and/or one or more passenger restraint systems. Alternatively or in addition, information from the above described algorithm may preferably be used to alert the driver by suited visual, acoustical or haptical warning means.